Hepatic Metabolic Derangements Triggered by Hyperthermia: An In Vitro Metabolomic Study
4. Materials and Methods 1. Chemicals
All reagents were of analytical grade or of the highest grade available. Antibiotic mixture of penicillin/streptomycin (10,000 U/mL/10,000 mg/mL), fungizone (250 mg/mL), and heat-inactivated fetal bovine serum (FBS) were obtained from GIBCO Invitrogen (Barcelona, Spain). Collagen G was obtained from Merck (Darmstadt, Germany). 4-Fluorobenzaldehyde (≥98%), collagenase fromClostridium histolyticumType IA, desmosterol (≥84%), dexamethasone, ethylene glycol-bis-(2-aminoethylether)-N, N, N’, N’-tetraacetic acid (EGTA), gentamicin, insulin solution from bovine pancreas (10 mg/mL), methoxyamine hydrochloride (≥98%),N,O-bis(trimethylsilyl)trifluoroacetamide with 1% trimethylchlorosilane (BSTFA + 1% TMCS), O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine hydrochloride (PFBHA,≥99%), sodium chloride (NaCl,≥99.5%), thiazolyl blue tetrazolium bromide (MTT,≥98%), thymol (≥98.5%), Triton X-100, trypan blue solution, Williams’ E medium, and all standards used throughout the work were purchased from Sigma-Aldrich (St. Louis, Missouri, USA).
Methanol (≥99.9%) and pyridine (≥99%) were purchased from VWR (Leuven, Belgium).
4.2. Isolation and Primary Culture of Mouse Hepatocytes
Ten male CD-1 mice (7–9 weeks old) were used in these experiments. Animal housing and experimental procedures were performed in accordance with Portuguese legislation (Decree-Law No. 113/2013, of August 7th), and approved by the Ethical Committee of the Faculty of Pharmacy of University of Porto (protocol number P158/2016) and by the Portuguese National Authority for Animal Health (reference number 0421/000/000/2017). Isolation of hepatocytes was performed using a modified collagenase perfusion method, as described by Godoy et al. [31]. Surgical procedures were performed under isoflurane anesthesia and carried out between 10.00 and 11.00 a.m. The initial viability of the isolated mouse hepatocytes was estimated by the trypan blue exclusion test and was always greater than 80%. Subsequently, a suspension containing 0.5×106viable cells/mL was prepared in complete culture medium (William’s E medium supplemented with 10% FBS, 100 U/mL penicillin, 100 mg/mL streptomycin, 100μg/mL gentamicin, 5μg/mL insulin, 50 nM dexamethasone, and 2.5μg/mL fungizone) and seeded into a collagen-coated 35-mm Petri dishes (for metabolomic studies) and 96-well culture plates (for cell viability assays). The cells were then incubated overnight at 37◦C with 5% CO2to allow cell adhesion. After seeding, the maintenance media was replaced by serum-free medium and the cells were incubated for 24 h under normothermic (37◦C) or hyperthermic (40.5◦C) conditions. For each 96-well plate, a positive control (1% Triton X-100) was also considered.
4.3. Cell Viability Assays
The effect of temperature on metabolic activity of PMH was determined using the MTT reduction assay, as described in a previous work [32]. In order to evaluate the effect of temperature in the cell membrane disruption, the release of lactate dehydrogenase (LDH) to the extracellular medium was assessed using a protocol previously described by Valente et al. [33]. For both assays data were normalized to a no-effect (PMH at 37◦C) and a maximum-effect (PMH lysed with 1% Triton X-100) controls.
4.4. Collection, Preparation, and Analysis of Samples for Metabolomic Analysis
The collection of samples was performed according to a protocol used in a previous study [32].
Briefly, for the analysis of the extracellular volatile fraction, the culture medium from each well was collected on ice and subsequently centrifuged (2000×g, 5 min, 4◦C) to eliminate possible cellular fragments. Adherent cells were washed twice with 0.9% NaCl, and then an ice-cold methanol:water solution (80:20, v/v) was added to extract the intracellular metabolites. In sequence, cells were scraped, harvested, sonicated on ice for a few seconds, and centrifuged for 10 min at 3000×gat 4◦C.
The supernatant was collected in a glass vial for further intracellular metabolome analysis. For each GC-MS procedure, quality control (QC) samples were prepared by pooling the same amount of each sample used in the study. All samples were kept at−80◦C until analysis.
The analysis of volatile fraction of the extracellular metabolome was performed by two methodologies based on headspace solid-phase microextraction (HS-SPME) coupled to GC-MS previously optimized by our group [34]. The analysis VOCs was carried out directly in the headspace of the cell culture medium, while VCCs were determined after a previous derivatization step.
Sample preparation and GC-MS analysis of samples is described in detail in previous studies of our group [32,34].
4.5. GC-MS Data Pre-Processing
The GC-MS data were converted to the CDF file format using the software MASSTransit 3.0.1.16 (Palisade Corp, Newfield, NY) and pre-processed using the software MZmine 2.23 [35]. The parameters used in the pre-processing steps were set as follows: RT range 4.3–24.5 min,m/zrange 50–400, MS data noise level 3×104, m/z tolerance 0.5, baseline level 8×104and peak duration range 0.02–0.35 min for the intracellular analysis; RT range 2.1–25.0 min,m/zrange 40–300, MS data noise level 1×105,m/z
tolerance 0.5, baseline level 4×104and peak duration range 0.02–0.3 for the VOCs analysis; and RT range 10.5–35.5 min,m/zrange 50–500, MS data noise level 1×105,m/ztolerance 0.5, baseline level 2×104and peak duration range 0.02–0.5 min for the VCCs analysis. After pre-processing steps, data were normalized by total chromatogram area to eliminate systematic and biological bias [36].
All known artefacts including peaks from the chromatographic column, SPME fibers (e.g., phthalates and siloxanes) and plasticizers, as well as chromatographic peaks with a signal to noise less than three and with relative standard deviation (RSD) higher than 30% across all QCs, were not considered in the statistical analysis.
4.6. Multivariate and Univariate Statistical Analysis
The final matrices were imported into the SIMCA-P 13.0.3 software (Umetrics Umea, Sweden) for multivariate analysis. Principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were applied to Pareto scaled data, with a default 7-fold internal cross validation, from which R2and Q2values reflect, respectively, the explained variance and the predictive capability of the models [37]. Simultaneously, all OPLS-DA models were validated through permutation test (500 permutations) and CV-ANOVAp-value (cross-validated analysis of variance) were also performed to determine the level of significance of group separation, ap-value
<0.05 being indicative of a significant model [37]. The variables (m/z-RT pairs) relevant for groups separation were assessed through the inspection of loadingS-plots. Only the variables corresponding to the metabolite fingerprint (based on relative abundance and selectivity) and that simultaneously presented variables importance to the projection (VIP)>1 and p(corr)>|0.5|were used in subsequent univariate analysis [37]. In addition, metabolites that resulted in multiple chromatographic peaks as a consequence of derivatization reactions were summed, as suggested by Mastrangelo et al. [38].
The statistical significance between the mean of two groups under study (PMH under normothermic vs. hyperthermic conditions) was assessed for the relevant metabolites (|p(corr)| >0.5 and VIP>1) in GraphPad Prism version 6 (GraphPad Software, San Diego, CA, USA). Thep-value was determined through an unpaired studentt-test for normal distribution data or an unpaired Mann–Whitney test for a non-normal distribution. False discovery rate (FDR) correctedp-values were considered in the assessment of statistical significance [39]. Additionally, the effect size (ES), corrected for a small number of samples, were also determined for each relevant metabolite, according to equations provided in the literature [40].
4.7. Identification of Discriminant Metabolites
The identification of discriminant metabolites was done according to the Metabolomics Standards Initiative (MSI) guidelines, being based on the comparison of the retention index (RI) determined for each metabolite with the RI described in the literature and by comparing the retention time (RTs) and mass spectrum of the discriminant metabolite with spectra accessible in the National Institute of Standards and Technology (NIST14) mass spectral library [14]. Only for reverse match factors greater than 700, the tentative metabolite identification was considered. Whenever possible, the identification was unequivocally confirmed with authentic reference standards injected under the same chromatographic conditions. Metabolites that do not meet these identification criteria are reported throughout the paper according to their crescent RT values as ‘IMi’ (for the intracellular metabolites),
‘VOCi’ or ‘VCCi’ (i=1, 2, 3. . .).
4.8. Biochemical Interpretation
Metabolic pathway analysis was used to identify biochemical pathways associated with alterations caused by the temperature increase. Metabolites significantly altered (p<0.05) with Human Metabolome Database (HMDB) codes were imported into a Metaboanalyst 4.0 software (http://www.metaboanalyst.
ca) and were searched againstMus musculusdatabase [41]. Biochemical pathways were selected according to thep-value (p<0.05) and pathway impact value (>0.1). The Human Metabolome Database
(HMDB,www.hmdb.ca) and Kyoto Encyclopedia of Genes and Genomes (KEGG,www.kegg.jp) were also checked to support the biochemical interpretation. Furthermore, to search for possible correlations between metabolites significantly altered (p<0.01), Spearman’s rank correlation coefficient was also calculated and represented in a heatmap.
5. Conclusions
Heat stress is a life-threatening condition capable of disturbing cellular homeostasis. In this work, we presented a metabolomic study of the liver following hyperthermia in an in vitro model.
Our data revealed that GC-MS metabolomic profiling can be successfully used to visualize the hyperthermia-induced disorders, since in the present study prominent derangements were observed in the intra and extracellular hepatic metabolome. Multivariate and univariate statistical analysis revealed a high number of compromised metabolites that are essentially associated with the energetic pathway, synthesis of antioxidant defenses, and with the lipid peroxidation process. Taking into account the results obtained, it is our belief that this metabolomic study may represent an interesting platform to evaluate and understand the deleterious effects of heat stroke in humans.
Supplementary Materials:The following are available online athttp://www.mdpi.com/2218-1989/9/10/228/s1, Figure S1: Principal component analysis (PCA) score scatter plots obtained for the GC-MS chromatograms of the three distinct procedures ((A) intracellular metabolite profiling, (B) extracellular metabolite profiling—VOCs and (C) extracellular metabolite profiling—VCCs) to evaluate data quality. Each sample is represented in the score’s scatter plot as an individual variable, namely quality control (QC) samples () and the intracellular/extracellular content of all primary mouse hepatocytes samples (). (D) Boxplot of the three internal standards used in the different metabolomics studies. Data are expressed as the mean and standard deviation (SD) of the normalized peak area by total area of the chromatogram. All internal standards presented a variation coefficient inferior to 20%.; Figure S2: PCA score scatter plots obtained for the chromatograms corresponding to cells exposed to normothermic (n=10,) and hyperthermic (n=10,) conditions, after analysis of the (A) intracellular metabolome, as well as (B) VOCs and (C) VCCs present in the extracellular metabolome.; Table S1. Identification of discriminant intracellular metabolites selected from OPLS-DA loadingS-plots (VIP>1 and|p(corr)| >0.5).
The identification of the metabolites was done according to the Metabolomics Standards Initiative (MSI) levels.
They were characterized by retention time (RT), characteristic ions (m/z), retention index (from the literature (RIlit) and compared with the calculated (RIcalc) for the same chromatographic column), reverse match factor from National Institute of Standards and Technology (NIST) and Human Metabolome Database (HMDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG) code (when available). Table S2. Identification of discriminant volatile extracellular metabolites (VOCs and VCCs) selected from OPLS-DA loadingS-plots (VIP>1 and|p(corr)|
>0.5). The identification of the metabolites was done according to the MSI levels. They were characterized by retention time (RT), characteristic ions (m/z), retention index (from the literature (RIlit) and compared with the calculated (RIcalc) for the same chromatographic column), reverse match factor from NIST and HMDB and KEGG code (when available).
Author Contributions:A.M.A. was responsible for the execution of the experimental work, data analysis and writing the manuscript. M.E. helped with the isolation of primary mouse hepatocytes. F.C., M.d.L.B., M.C.
and P.G.d.P. contributed to the design and management of the study. All authors critically commented on and approved the final submitted version of the paper.
Funding: This research was funded by European Union (FEDER funds POCI/01/0145/FEDER/007728) and National Funds (FCT/MEC, Fundação para a Ciência e a Tecnologia and Ministério da Educação e Ciência) under the Partnership Agreement PT2020 UID/MULTI/04378/2019. The study is a result of the project NORTE-01-0145-FEDER-000024, supported by Norte Portugal Regional Operational Program (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement (DESignBIOtecHealth—New Technologies for three Health Challenges of Modern Societies: Diabetes, Drug Abuse and Kidney Diseases), through the European Regional Development Fund (ERDF). A. M. Araújo and M. Enea thank FCT for their PhD fellowships (SFRH/BD/107708/2015 and PD/BD/109634/2015, respectively) and M. Carvalho also acknowledges FCT through the UID/MULTI/04546/2019 project.
Conflicts of Interest:The authors declare no conflict of interest.
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